An architecture for learning to behave

Abstract

The SAM architecture is a novel neural network architecture, based on the cerebral neocortex, for combining unsupervised learning modules. When used as part of the control system for an agent, the architecture enables the agent to learn the functional semantics of its motor outputs and sensory inputs, and to acquire behavioral sequences by imitating other agents (learning by 'watching'). This involves attempting to recreate the sensory sequences the agent has been exposed to. The architecture scales well to multiple motor and sensory modalities, and to more complex behavioral requirements. The SAM architecture may also hint at an explanation of several features of the operation of the cerebral neocortex.<>